





"""simple docstring"""
import math
import os
import sys

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase    				= ''
 try:
  with open(_UpperCAmelCase  ,     'rb'			) as binary_file:
   lowerCAmelCase    				= binary_file.read()
  for dat in data:
   lowerCAmelCase    				= F'{dat:08b}'
   result += curr_byte
  return result
 except OSError:
  print('File not accessible'			)
  sys.exit()

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    dict[str, str]  ,     _UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    str			):
 lexicon.pop(_UpperCAmelCase			)
 lowerCAmelCase    				= last_match_id

 if math.loga(_UpperCAmelCase			).is_integer():
  for curr_key in lexicon:
   lowerCAmelCase    				= '0' + lexicon[curr_key]

 lowerCAmelCase    				= bin(_UpperCAmelCase			)[2:]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase    				= {'0': '0', '1': '1'}
 lowerCAmelCase				,lowerCAmelCase    				= '', ''
 lowerCAmelCase    				= len(_UpperCAmelCase			)

 for i in range(len(_UpperCAmelCase			)			):
  curr_string += data_bits[i]
  if curr_string not in lexicon:
   continue

  lowerCAmelCase    				= lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(_UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase			)
  index += 1
  lowerCAmelCase    				= ''

 while curr_string != "" and curr_string not in lexicon:
  curr_string += "0"

 if curr_string != "":
  lowerCAmelCase    				= lexicon[curr_string]
  result += last_match_id

 return result

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    str			):
 lowerCAmelCase    				= os.path.getsize(_UpperCAmelCase			)
 lowerCAmelCase    				= bin(_UpperCAmelCase			)[2:]
 lowerCAmelCase    				= len(_UpperCAmelCase			)

 return "0" * (length_length - 1) + file_length_binary + compressed

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    str			):
 lowerCAmelCase    				= 8
 try:
  with open(_UpperCAmelCase  ,     'wb'			) as opened_file:
   lowerCAmelCase    				= [
       to_write[i : i + byte_length]
       for i in range(0  ,     len(_UpperCAmelCase			)  ,     _UpperCAmelCase			)
   ]

   if len(result_byte_array[-1]			) % byte_length == 0:
    result_byte_array.append('10000000'			)
   else:
    result_byte_array[-1] += "1" + "0" * (
        byte_length - len(result_byte_array[-1]			) - 1
    )

   for elem in result_byte_array:
    opened_file.write(int(_UpperCAmelCase  ,     2			).to_bytes(1  ,     byteorder='big'			)			)
 except OSError:
  print('File not accessible'			)
  sys.exit()

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    str			):
 lowerCAmelCase    				= read_file_binary(_UpperCAmelCase			)
 lowerCAmelCase    				= compress_data(_UpperCAmelCase			)
 lowerCAmelCase    				= add_file_length(_UpperCAmelCase  ,     _UpperCAmelCase			)
 write_file_binary(_UpperCAmelCase  ,     _UpperCAmelCase			)


if __name__ == "__main__":
       compress(sys.argv[1], sys.argv[2])








import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=99  ,      _lowercase=13  ,      _lowercase=16  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=2  ,      _lowercase=32  ,      _lowercase=4  ,      _lowercase=4  ,      _lowercase=30  ,      _lowercase=0  ,      _lowercase=1  ,      _lowercase=2  ,      _lowercase=None  ,      )     -> List[str]:
									a_						:						Union[str, Any] 							= parent
									a_						:						Dict 							= batch_size
									a_						:						Any 							= decoder_seq_length
									# For common tests
									a_						:						Any 							= self.decoder_seq_length
									a_						:						int 							= is_training
									a_						:						List[Any] 							= use_attention_mask
									a_						:						List[str] 							= use_labels

									a_						:						Optional[Any] 							= vocab_size
									a_						:						Dict 							= d_model
									a_						:						Any 							= d_model
									a_						:						List[Any] 							= decoder_layers
									a_						:						List[str] 							= decoder_layers
									a_						:						Union[str, Any] 							= decoder_ffn_dim
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Tuple 							= eos_token_id
									a_						:						Union[str, Any] 							= bos_token_id
									a_						:						Dict 							= pad_token_id
									a_						:						Union[str, Any] 							= decoder_start_token_id
									a_						:						List[Any] 							= use_cache
									a_						:						List[str] 							= max_position_embeddings

									a_						:						int 							= None
									a_						:						Any 							= decoder_seq_length
									a_						:						Union[str, Any] 							= 2
									a_						:						Optional[int] 							= 1





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[int] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Optional[int] 							= None
									if self.use_attention_mask:
												a_						:						List[Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      vocab_size=2    )

									a_						:						List[Any] 							= None
									if self.use_labels:
												a_						:						Union[str, Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Any 							= TrOCRConfig(
									    vocab_size=self.vocab_size  ,      d_model=self.d_model  ,      decoder_layers=self.decoder_layers  ,      decoder_ffn_dim=self.decoder_ffn_dim  ,      decoder_attention_heads=self.decoder_attention_heads  ,      eos_token_id=self.eos_token_id  ,      bos_token_id=self.bos_token_id  ,      use_cache=self.use_cache  ,      pad_token_id=self.pad_token_id  ,      decoder_start_token_id=self.decoder_start_token_id  ,      max_position_embeddings=self.max_position_embeddings  ,      )

									return (config, input_ids, attention_mask, lm_labels)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> List[Any]:
									a_						:						Optional[int] 							= True
									a_						:						List[str] 							= TrOCRDecoder(config=_lowercase    ).to(_lowercase    ).eval()
									a_						:						str 							= input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									a_						:						Dict 							= model(_lowercase  ,      use_cache=_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase  ,      use_cache=_lowercase    )

									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    )    )
									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    ) + 1    )

									a_						:						Dict 							= outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									a_						:						List[str] 							= ids_tensor((2, 1)  ,      config.vocab_size - 1    ) + 1

									# append to next input_ids and
									a_						:						int 							= torch.cat([input_ids, next_tokens]  ,      dim=-1    )

									a_						:						Dict 							= model(_lowercase    )["""last_hidden_state"""]
									a_						:						List[str] 							= model(_lowercase  ,      past_key_values=_lowercase    )["""last_hidden_state"""]

									# select random slice
									a_						:						Optional[Any] 							= ids_tensor((1,)  ,      output_from_past.shape[-1]    ).item()
									a_						:						Union[str, Any] 							= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									a_						:						Any 							= output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						List[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						List[Any] 							= config_and_inputs

									a_						:						str 							= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict







@require_torch
class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						_A    :				Optional[int]       					=			(TrOCRForCausalLM,) if is_torch_available() else ()
						_A    :				Tuple       					=			{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
						_A    :				List[str]       					=			True
						_A    :				List[str]       					=			False





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						str 							= TrOCRStandaloneDecoderModelTester(self  ,      is_training=_lowercase    )
									a_						:						str 							= ConfigTester(self  ,      config_class=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						def 			UpperCamelCase__					(    self    )     -> int:
									pass





						def 			UpperCamelCase__					(    self    )     -> str:
									pass





						def 			UpperCamelCase__					(    self    )     -> Dict:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						int 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									return





						@unittest.skip("""The model doesn't support left padding"""    )  # and it's not used enough to be worth fixing :)
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass


